Think Bayes [Kindle Edition] Author: | Language: English | ISBN:
B00F5BS96Q | Format: PDF, EPUB
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If you know how to program with Python and also know a little about probability, you’re ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical notation, and use discrete probability distributions instead of continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer, and you’ll begin to apply these techniques to real-world problems.
Bayesian statistical methods are becoming more common and more important, but not many resources are available to help beginners. Based on undergraduate classes taught by author Allen Downey, this book’s computational approach helps you get a solid start.
- Use your existing programming skills to learn and understand Bayesian statistics
- Work with problems involving estimation, prediction, decision analysis, evidence, and hypothesis testing
- Get started with simple examples, using coins, M&Ms, Dungeons & Dragons dice, paintball, and hockey
- Learn computational methods for solving real-world problems, such as interpreting SAT scores, simulating kidney tumors, and modeling the human microbiome.
Direct download links available for Think Bayes [Kindle Edition] Free PDF
- File Size: 4444 KB
- Print Length: 210 pages
- Simultaneous Device Usage: Unlimited
- Publisher: O'Reilly Media; 1 edition (September 12, 2013)
- Sold by: Amazon Digital Services, Inc.
- Language: English
- ASIN: B00F5BS96Q
- Text-to-Speech: Enabled
X-Ray:
- Lending: Not Enabled
- Amazon Best Sellers Rank: #41,172 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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in Kindle Store > Kindle eBooks > Nonfiction > Science > Mathematics > Mathematical Analysis - #1
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in Kindle Store > Kindle eBooks > Nonfiction > Science > Mathematics > Research
Should you buy this book given that the only other review as of this time is a negative review (based on the lack of a table of contents)? Hmm, that is exactly the sort of decision analysis that is covered by this book. Should you wait for the next train or catch a taxi instead? Or what about the classic Monty Hall problem where there is car hidden behind one of three doors in a TV game show? The contestant picks a door, but prior to opening it, the host opens another door which does not contain the car and then offers the contestant the opportunity to 'stick' to his current selection or 'switch' to the other door. Should the contestant 'stick' or 'switch'? Bayes's Theorem provides a rationale for making this decision and this book covers all of this and more.
This is a great book and a good introduction to the application of Bayes's Theorem in a number of scenarios. The theoretical aspects are well accessible and the Python code is sufficiently clear. This is not an introduction to Python and readers should be relatively familiar with Python or other high level languages to make the most out of this book.
The PDF for the book is freely available from Green Tea Press. If you are concerned about the lack of a table of contents in the mobi version, get the paper copy until this is resolved... I would highly recommend it.
By Ricardo Dapaz
I used the greenteapress version to teach an intro class on Bayes before this paper version was released. It worked great (but PayPal rejected my donation). Straightforward understandable examples allowed me to keep more complicated examples in context. This in conjunction with CamDavidsonPilon's excellent community book really gave a well-rounded class.
By Michael Morgan
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